In particular, spectral clustering is one of the key graph clustering methods [1]}. In its most basic form, this algorithm consists in partitioning a graph into two communities using the eigenvector associated with the second smallest eigenvalue of the graph's Laplacian matrix (the so-called Fiedler vector [2]}).
Spectral clustering is popular, as it is an efficient relaxation of the NP-hard problem of cutting the graph into two balanced clusters so that the weight between the two clusters is minimal [1]}.
